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Simultaneous Multi-Slice Acceleration of Multi-Echo EPI Provides Rapid and Accurate Quantitative Susceptibility Mapping
Oliver C. Kiersnowski1, Patrick Fuchs1, Stephen J. Wastling2,3, John S. Thornton2,3, and Karin Shmueli1
1Department of Medical Physics & Biomedical Engineering, University College London, London, United Kingdom, 2UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 3Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom

Synopsis

Simultaneous multi-slice (SMS) acquisition is increasingly used to accelerate echo planar imaging (EPI). EPI acquisitions have been used for quantitative susceptibility mapping (QSM) but, to utilise SMS, an investigation into the effect of SMS on EPI-QSM accuracy is necessary. Here, we show that SMS has no significant effect on magnetic susceptibility maps and values, and can, therefore, provide accurate QSM within a short TR. We also show, for the first time, that multi-echo phase images can be acquired using an EPI sequence (highly) accelerated using SMS and parallel imaging, leading to more accurate QSM reconstruction compared to standard single-echo EPI.

Introduction

Echo-planar imaging (EPI) has been used to acquire data for quantitative susceptibility mapping (QSM)1–3 in good agreement with conventional 3D GRE sequences4. Simultaneous multi-slice (SMS)5–7 acquisition is increasingly used for acceleration in EPI applications such as functional MRI (fMRI)8,9 but not for QSM, although SMS has been used for QSM in 2D GRE sequences10. The effect of SMS on EPI-QSM accuracy has not been investigated. Furthermore, although multi-echo GRE acquisition has been shown to be more accurate than single-echo for QSM11 and fMRI12,13 no studies have been published using multi-echo EPI for QSM.

Therefore, we compared the effect of SMS with different multiband (MB) acceleration factors on the accuracy of QSM acquired using multi-echo EPI.

Methods

Acquisition:
EPI brain images of a healthy volunteer were acquired on a 3T Siemens Prisma MR System using a 64-channel head coil and a multi-band EPI sequence14 at 2mm isotropic resolution with five echoes; matrix size = 120×120×96; 6/8 partial Fourier; BW=1984 Hz/Px; FA=90°; fat saturation; transverse orientation; interleaved slice acquisition and SENSE coil combination15. We compared MB factors of 1 (no acceleration), 2, 3 and 4 with GRAPPA (R) factors 2 and 4 in the first PE direction, and acquired five volumes per sequence. TEs and sequence-specific parameters are shown in Figure 1. To reduce slice-leakage artefacts, LeakBlock kernel optimisation was used16. Unaliasing of slices was carried out with blipped-CAIPI17.

A T1-weighted structural image for ROI segmentation was acquired using a whole-brain MPRAGE sequence with 1mm isotropic resolution; TR/TE=2000/2.05 ms; matrix size = 256×240×256; R=2; FA=8°.

QSM Pipeline:
For each volume, total field maps and noise maps were obtained from a weighted non-linear fit18,19 of the complex data over echoes. The number of echoes (3-5) for the multi-echo fit was optimised by comparing susceptibility maps for each number. To compare multi-echo v. single-echo EPI-QSM, the following pipeline was also applied to scaled single-echo phase images. Brain masks were calculated using BET19,20 on the magnitude images (first echo for multi-echo), and eroded by three voxels (only for the first echo for single-echo). The masks were multiplied with a (mean-thresholded) inverse noise map from the nonlinear fit to remove noisy voxels in the outer four layers. Residual phase wraps were removed using Laplacian unwrapping19,21, background fields in each slice were removed using 2D V-SHARP22,23 (kernel radius 20mm), and through-slice harmonic background fields were removed using projection onto dipole fields (PDF)19,24. Susceptibility ($$$\chi$$$) was calculated using iterative fitting with Tikhonov regularisation25 ($$$\alpha=0.006$$$).

Analysis:
All first-echo magnitude images were rigidly registered to the first-echo magnitude image of the reference volume (MB=1, R=2) using NiftyReg26. Susceptibility maps were then registered into the same space using the resulting transformation matrices. Eight regions of interest (ROIs) in the deep gray matter (Fig. 4) were obtained by segmenting the T1-weighted image using GIF27–29, and were then non-rigidly registered to the first-echo magnitude image of the reference volume. For each sequence and TE, temporal signal-to-noise ratio (tSNR) maps were calculated as the mean voxel values over the standard deviation of the magnitude images over the five volumes. ROI mean $$$\chi$$$ values were compared in multi-echo susceptibility maps (TE1 to 3, for R=2 and TE1 to 5, for R=4), averaged over the five volumes. Kruskal-Wallis tests were carried out to investigate statistically significant differences in (non-normally distributed) ROI mean values. The post-hoc Dunn’s test30 was used to identify whether $$$\chi$$$ values for specific MB factors differed significantly from the reference. Bland-Altman plots were used to investigate whether MB acceleration introduced any systematic $$$\chi$$$ bias.

Results

As observed previously, higher GRAPPA factors allow the acquisition of more echoes in a shorter time with an associated tSNR cost but MB factors do not reduce tSNR17 (Figure 2).

The optimal number of echoes for multi-echo reconstruction was three for R=2 and five for R=4. Similar to 3D-GRE11, multi-echo QSM was found to be more accurate than single-echo QSM with 2D-EPI (Figure 3).

There were no consistent structural differences between susceptibility maps acquired with different MB factors (Figure 4). Overall, for different MB factors, ROI mean susceptibility values were not significantly different (Figures 5a, 5b) and no bias was observed between prescribed limits of agreement of 0.01 ppm11 (Figures 5c, 5d).

Discussion and Conclusion

Although it is well known that MB acceleration reduces TR in EPI without reducing the tSNR, we even observed increased tSNR near the brain edges (e.g., Figure 2, MB = 3), probably due to synergistic interaction between the RF coil arrangement and certain MB factors.

As well as reducing distortion and drop-out, higher GRAPPA acceleration factors allow acquisition of more TEs within the same TR, which improved multi-echo QSM reconstruction, as shown by the increased $$$\chi$$$ values in the globus pallidus at R=4 v. R=2 (Figures 4 and 5 b v. a). MB acceleration did not introduce any systematic bias or significantly affect susceptibility estimates, therefore, EPI with SMS acceleration can be used to provide accurate QSM.

As for QSM with 3D-GRE11, we have shown that multi-echo QSM with EPI, with GRAPPA and SMS acceleration, is more accurate than single-echo QSM. EPI with SMS acceleration can be used to provide accurate QSM within a short TR.

Acknowledgements

Oliver Kiersnowski’s work was supported by the EPSRC-funded UCL Centre for Doctoral Training in Intelligent, Integrated Imaging in Healthcare (i4health) (EP/S021930/1). John Thornton received support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. Karin Shmueli and Patrick Fuchs were supported by European Research Council Consolidator Grant DiSCo MRI SFN 770939.

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Figures

Figure 1: Acquisition parameters for 2D-EPI with various multiband (MB) and GRAPPA acceleration factors. GRAPPA acceleration was always along the PE direction (A>>P). The (MB + GRAPPA) acceleration lowers the TR. The total acquisition time for five volumes includes additional reference scans required for SMS acceleration.

Figure 2: Temporal SNR maps for R = 2 and 4, across all multiband (MB) factors. As expected, R = 2 had higher tSNR than R= 4 at an approximately equal echo time but R = 4 allowed more echoes to be acquired within the same amount of time.

Figure 3: Optimal multi-echo (ME) χ maps (left, top rows) followed by single-echo χ maps at each TE for both R=2 and 4 at MB=2. Corresponding difference images (single-echo – optimal multi-echo) are shown (bottom row). Longer TE single-echo χ maps failed due to severe distortion and signal dropout. Multi-echo χ maps have fewer residual background field artefacts and are less affected by geometric distortion. Green boxes indicate optimal single-echo χ maps.

Figure 4: Susceptibility maps (top rows) at different MB acceleration factors at R=2 (top) and R=4 (bottom). Difference images are shown relative to maps with no SMS (bottom rows). Axial and coronal slices are shown. No consistent regional susceptibility differences are apparent between different MB factors.

Figure 5: ROI mean χ values for all MB factors with R=2 (a) and R=4 (b). * indicates p < 0.05 for post-hoc Dunn’s test. In general, SMS acceleration did not significantly affect χ values. R=4 reduced distortion and signal drop-out, resulting in higher χ values in some ROIs (e.g. globus pallidus). Bias from Bland-Altman analyses for R=2 (c) and R=4 (d) indicates no systematic bias in ROI mean χ for all MB factors between prescribed limits of agreement of 0.01 ppm.

Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)
2361
DOI: https://doi.org/10.58530/2022/2361